Self-collision Avoidance Method for a Dual-arm Robot

Kabanov, Aleksey; Tokarev, Denis A.

doi:10.1109/cts48763.2019.8973236

Cited by 2 publications

(3 citation statements)

References 5 publications

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“…In favor of such a choice of the input dataset for training the neural network is the effectiveness that is obtained when using such a neural network to solve the inverse kinematics problem (some results on this issue can be found in [12]).…”

Section: The Input Data For the Training Setmentioning

confidence: 99%

“…Vadim Kramar, Self-Collision Avoidance Control of Dual-Arm Multi-Link Robot Using Neural Network Approach and the search of the coordinates and their combinations at which these critical positions arise [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. In [7], the authors presented a solution of the problem of two manipulators motion control using a constraint-based programming approach, which was used to generate online motion plans.…”

Section: Introductionmentioning

confidence: 99%

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Self-Collision Avoidance Control of Dual-Arm Multi-Link Robot Using Neural Network Approach

Kramar

Kabanov

2022

Journal of Robotics and Control

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The problem of mutual collisions of manipulators of a dual-arm multi-link robot (so-called self-collisions) arises during the performance of a cooperative technological operation. Self-collisions can lead to non-fulfillment of the technological operation or even to the failure of the manipulators. In this regard, it is necessary to develop a method for online detection and avoidance of self-collisions of manipulators. The article presents a method for detecting and avoiding self-collisions of multi-link manipulators using an artificial neural network by the example of the dual-arm robot SAR-401. A comparative analysis is carried out and the architecture of an artificial neural network for self-collisions avoidance control of dual-arm robot manipulators is proposed. The novelty of the proposed approach lies in the fact that it is an alternative to the generally accepted methods of detecting self-collisions based on the numerical solution of inverse kinematics problems for manipulators in the form of nonlinear optimization problems. Experimental results performed based on MATLAB model, the simulator of the robot SAR-401 and on the real robot itself confirmed the applicability and effectiveness of the proposed approach. It is shown that the detection of possible self-collisions using the proposed method based on an artificial neural network is performed approximately 10 times faster than approaches based on the numerical solution of the inverse kinematics problem while maintaining the specified accuracy.

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Section: The Input Data For the Training Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-Collision Avoidance Control of Dual-Arm Multi-Link Robot Using Neural Network Approach

Kramar

Kabanov

2022

Journal of Robotics and Control

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“…Several researchers opted to discretize the operation space into small discrete volumes, and used the master-slave and occupied-free models, along with other methods to work [8,9]. Zhou et al [10] proposed a collision-free compliance control strategy based on the physical constraints.…”

Section: Introductionmentioning

confidence: 99%

An effective self-collision detection algorithm for multi-degree-of-freedom manipulator

Liu

Zhang²,

Qin³

et al. 2022

Meas. Sci. Technol.

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In order to address the self-collision problem associated with the operation of modern industrial robots, this paper proposes a multi-degree-of-freedom (multi-DOF) collision detection algorithm that can detect self-collision in single arm and double arm robots as well as collision with the load. Firstly, the zero pose of the Denavit-Hartenberg model is built based on the manipulator configuration, and the coordinate information of each key point is obtained through a rotation and translation operation of the matrix. Then, the positional relation and distance between the detected objects are determined by the spatial geometry theory, and finally, collision is detected using a collision matrix. By simulating two groups of single arms and two groups of double arms, and from the laboratory testing of SR10C in the SIASUN robot factory, it has been verified that the proposed algorithm has good collision detection capability. Without the use of sensors, cameras, and other external devices, the collision between the arm and the load, and the collision between the cooperative robot and the load may be effectively detected and mitigated.

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Self-collision Avoidance Method for a Dual-arm Robot

Cited by 2 publications

References 5 publications

Self-Collision Avoidance Control of Dual-Arm Multi-Link Robot Using Neural Network Approach

Self-Collision Avoidance Control of Dual-Arm Multi-Link Robot Using Neural Network Approach

An effective self-collision detection algorithm for multi-degree-of-freedom manipulator

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